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  Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning

Banerjee, A., Iscen, A., Kremer, K., & Kukharenko, O. (2023). Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning. The Journal of Chemical Physics, 159(7): 074108. doi:10.1063/5.0151156.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-A3B3-0 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-A3B4-F
Genre: Journal Article

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TheJournalofChemicalPhysics-074108_1_5.0151156.pdf (Publisher version), 7MB
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https://hdl.handle.net/21.11116/0000-000D-A3B5-E
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TheJournalofChemicalPhysics-074108_1_5.0151156.pdf
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Copyright Date:
2023
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http://creativecommons.org/licenses/by/4.0/

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 Creators:
Banerjee, Atreyee1, Author           
Iscen, Aysenur1, Author           
Kremer, Kurt1, Author           
Kukharenko, Oleksandra1, Author           
Affiliations:
1Dept. Kremer: Polymer Theory, MPI for Polymer Research, Max Planck Society, ou_1800287              

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Language(s): eng - English
 Dates: 2023-08-212023
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1063/5.0151156
 Degree: -

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Project name : APACHE
Grant ID : 814496
Funding program : H2020-NMBP-ST-IND-2018-2020 (H2020)
Funding organization : European Commission (EC)

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Title: The Journal of Chemical Physics
  Abbreviation : J. Chem. Phys.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Institute of Physics
Pages: - Volume / Issue: 159 (7) Sequence Number: 074108 Start / End Page: - Identifier: ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226